{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# import seaborn\n",
    "import os\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f92ab864050>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fk7p2RUTKS0WUH+7udwJ3QnBT7KxtrcA7MteZ2WeAf5rZDHdfamZNwBnAye7+13Cf04Hn\nzOwwd3+kAF9jOxq1KyJSXqJukQ5XE+BAS/j6ICAF3Jvewd2fB5YCh+/sIGZWZWaN6QVoyFeBmmtX\nRKS8FE2Qmlk18G3g1+7eFq6eBHS7e0vW7mvCbTszD2jNWJbnq06dIxURKS9FEaRmlgJuAgz4VB4O\neTFB6za9TMvDMQHNbCQiUm4iPUc6FBkhujvwbxmtUYDVQKWZNWe1SieG2wbl7l1AV8Zn5K3egXOk\nuvxFRKQsxLpFmhGic4C3u/uGrF0eBXqAYzLeszcwA3i4UHVmqtRgIxGRshJpi9TM6oE9M1bNMrMD\ngY3AKuB3BJe+vAtImln6vOdGd+9291Yz+zlwiZltBNqAK4CHoxixC5nnSNW1KyJSDqLu2j0Y+FvG\n60vCx+uAC4F3h6+fyHrf24D7wuefA/qBm4Eq4G7g0/kvdWh0+YuISHmJ+jrS+wgGEO3Ma568dPdO\nggkdBpvUoeA0ab2ISHmJ9TnSYrRt1K6CVESkHChI82zbqF2dIxURKQcK0jzThAwiIuVFQZpn6ctf\nevud3j6FqYhIqVOQ5lm6axegW0EqIlLyFKR5tl2QqntXRKTkKUjzrCKZIJkIrtrReVIRkdKnIB0F\nmm9XRKR8KEhHwbbZjXQJjIhIqVOQjgJNXC8iUj4UpKNAE9eLiJQPBeko0MT1IiLlQ0E6CjTfrohI\n+VCQjoKBrl2N2hURKXkK0lGgUbsiIuVDQToKNGpXRKR8KEhHgQYbiYiUDwXpKNh2jlRduyIipU5B\nOgrSLVLd/UVEpPQpSEfBwOUvGrUrIlLyFKSjYNvMRgpSEZFSpyAdBbr8RUSkfChIR4EufxERKR8K\n0lGgmY1ERMqHgnQUaNSuiEj5iDRIzexIM7vdzFaamZvZSVnbzcwuMrNVZtZhZvea2ZysfarN7Coz\n22BmW8zsZjObWNhvsr1to3Z1jlREpNRF3SKtA54Ezt7J9i8A5wBnAYcC7cDdZladsc+lwInA+4Gj\ngCnA70er4KHQqF0RkfJREeWHu/udwJ0AZrbdNgtWnAt8w91vC9d9FFgDnAT8xsyagDOAk939r+E+\npwPPmdlh7v5Iob5LJo3aFREpH1G3SHdlFjAJuDe9wt1bgfnA4eGqg4BU1j7PA0sz9tmBmVWZWWN6\nARryWbhG7YqIlI84B+mk8HFN1vo1GdsmAd3u3rKLfQYzD2jNWJbnVur2BlqkGrUrIlLy4hyko+li\noCljmZbPg6fPkWrUrohI6YtzkK4OH7NH4E7M2LYaqDSz5l3sswN373L3tvQCbM5HwWkDo3Z1jlRE\npOTFOUgXE4ThMekV4fnMQ4GHw1WPAj1Z++wNzMjYp+DUtSsiUj4iHbVrZvXAnhmrZpnZgcBGd19q\nZpcBXzazlwiC9evASuBWCAYfmdnPgUvMbCPQBlwBPBzViF3Q5S8iIuUk0iAFDgb+lvH6kvDxOuA0\n4DsE15r+FGgGHgTe6e6dGe/5HNAP3AxUAXcDnx7Vql+DLn8RESkfUV9Heh9gu9juwFfDZWf7dBJM\n6LCzSR0Krirj8hd33+EaWRERKR1xPkdatNJdu+7Q0+cRVyMiIqNJQToK0qN2QZfAiIiUurwFqZl9\n08yuydfxilllctuPVRPXi4iUtny2SKcCM/N4vKKVSNhAmGrkrohIacvbYCN3PzVfxyoFVRUJuvv6\nFaQiIiVO50hHSaUugRERKQtDapGa2TlDPaC7Xz7yckqHZjcSESkPQ+3a/dwQ93NAQQpUpTRxvYhI\nORhSkLr7rNEupNSoRSoiUh5yOkdqoXwVU0o0TaCISHkYUZCa2Rlm9jTQCXSa2dNm9vH8llbcNHG9\niEh5GPblL2Z2EXAe4V1WwtWHA5ea2Qx33+m8uOVE9yQVESkPI7mO9FPAme7+64x1fzCzhQThqiBl\n2+xGOkcqIlLaRtK1mwIWDLL+UaK/LVtspFukGrUrIlLaRhKkvyRolWb7BHBDbuWUjoFzpGqRioiU\ntJG2IM8ws2OBR8LXhwIzgF+YWfrm3Lj7eTnWV7Q0aldEpDyMJEjnAo+Fz/cIH9eHy9yM/cr6RpyZ\nN/cWEZHSNewgdfe3jUYhpSY9s5GCVESktOU6IcN0M5uer2JKybZRu+raFREpZcMOUjOrMLOvm1kr\nsARYYmatZvYNM0vlvcIipa5dEZHyMJJzpFcA7wO+wPYTMlwI7MbgI3rLzsDlLwpSEZGSNpIgPRn4\nkLvfmbFuoZktA36NghTQFIEiIuViJOdIuwi6dLMtBrpzqqaE6PIXEZHyMJIgvRL4iplVpVeEz78U\nbhMy59pVi1REpJSNpGv3DcAxwHIzezJcdwBQCfzFzH6f3tHd35d7icVJMxuJiJSHkQRpC3Bz1rpl\neailpAxc/qKuXRGRkjaSCRlOH41CBmNmSYLRwB8BJgErgWuBb7i7h/sY8L/AmUAz8BDwKXd/qVB1\nDkZduyIi5SGnCRkK4HyCUcCfAfYNX38B+GzGPl8AzgHOIpjztx2428yqC1vq9tJdu7r8RUSktMX9\ntmdvBm5z9z+Gr5eY2YeBN8FAa/RcghbqbeG6jwJrgJOA3wx20HBwVFXGqoZ8F64JGUREykPcW6T/\nAI4xs70AzOwA4C1A+hrWWQRdvvem3+DurcB8gkkidmYe0JqxLM934du6dnWOVESklMW9RfotoBF4\n3sz6gCTwJXdP3/d0Uvi4Jut9azK2DeZi4JKM1w3kOUw1aldEpDzEPUg/AJxCMJvSM8CBwGVmttLd\nrxvpQd29i2BiCQCCHuL8qlTXrohIWRhSkJrZOUM9oLtfPvJydvBd4Nvunj7X+ZSZ7U7QNXsdsDpc\nPxFYlfG+icATeaxj2NLnSLv7+unvdxKJ/Ie1iIhEb6gt0s8NcT8H8hmktUBv1ro+tp3bXUwQpscQ\nBqeZNRKM3v1RHusYtqaaFMmE0dfvrN3cxaSmSAcRi4jIKBlSkLr7rNEuZCduB74cToj/DMGsSucB\n14R1uZldFu7zEkGwfp3getNboyk5kEommDG2lsXr23ll3RYFqYhIiYr7qN3PAr8Dfgg8B3wP+Anw\nlYx9vkNwa7efAv8C6oF3untnYUvd0R7j6wBYtG5LxJWIiMhoGdFgIzObBrwbmEEwx+4Adz8vD3Wl\nj7WZ4DrRc3exjwNfDZdYmT2+Hp5byyvr2qMuRURERsmwg9TMjgH+ACwC9gGeBmYCBjyWz+KK3exx\nQYv0FbVIRURK1ki6di8Gvufu+wOdwH8A04H7gd/msbait8eEegAWqUUqIlKyRhKk+wK/CJ/3AjXu\nvoWga/X8fBVWCtIt0hUtHXR0a4YjEZFSNJIgbWfbedFVwB4Z28blXFEJGVtXSXNtCoDF69UqFREp\nRSMJ0kcI5rsF+BPwfTP7EsElKY/kq7BSYGYDrdJF63WeVESkFI0kSM8jmBQe4GvAX4APAkuAM/JT\nVumYPT44T/rKWrVIRURK0Uhu7L0o43k7wX1AZSf2CINULVIRkdI07Bapmf3MzI4ehVpK0uyBSRnU\nIhURKUUj6dodD9xlZsvM7LvhPUJlJwZapOu2EMwdISIipWTYQeru7wEmE8xpewjwmJk9Y2ZfNLOZ\n+S2v+M0YW0syYbR397Gmreu13yAiIkVlRHPtuvsmd/+pux8N7A5cC/x/wMv5K600VFYEk9eDZjgS\nESlFOU1ab2Yp4GCC25bNBNbkoaaSo8nrRURK14iC1MzeZmZXEwTntUAb8C5gWv5KKx0Dl8BowJGI\nSMkZyaT1K4CxwF3AJ4Db3V0n/3Yh3SJV166ISOkZyW3ULgR+6+4tea6lZM0er8nrRURK1UgmZLh6\nNAopZelpAle2BpPX11QmI65IRETyJafBRjI06cnr3TV5vYhIqVGQFoAmrxcRKV0K0gLReVIRkdKk\nIC2QPQYugVGLVESklChIC0ST14uIlCYFaYFo8noRkdKkIC0QTV4vIlKaFKQFkjl5vebcFREpHQrS\nAkpPFfjSWgWpiEipiH2QmtlUM7vezDaYWYeZPWVmB2dsNzO7yMxWhdvvNbM5Uda8M6+f1gzA315Y\nG3ElIiKSL7EOUjMbAzwE9ADHA/sBnwc2Zez2BeAc4CyC27m1A3ebWXVhq31t73r9ZAAefGk9G9u7\nI65GRETyIdZBCpwPLHP30939n+6+2N3/7O6vQNAaBc4FvuHut7n7QuCjwBTgpOjKHtzs8fXMndpI\nb79z59Oroi5HRETyIO5B+m5ggZn91szWmtnjZnZmxvZZwCTg3vQKd28F5gOH7+ygZlZlZo3pBWgY\npfp3cOLrpwDwhydWFuojRURkFMU9SGcDnwJeAo4DfgRcbmanhtsnhY9rst63JmPbYOYBrRnL8nwV\n/FredUAQpP9cspHVrZ2F+lgRERklcQ/SBPCYu3/R3R93958CVxOcD83FxUBTxjItx+MN2dTmGg7e\nfQzucMdCtUpFRIpd3IN0FfBs1rrngBnh89Xh48SsfSZmbNuBu3e5e1t6ATbno9iheveBQav09oU6\nTyoiUuziHqQPAXtnrdsLeDV8vpggMI9JbwzPeR4KPFyIAkfi+LmTSRg8uayFVzdo7l0RkWIW9yC9\nFDjMzL5oZnua2cnAJ4CrADyYtPYy4Mtm9m4z2x/4BbASuDWqol/L+IYqjthzHAB3qFUqIlLUYh2k\n7v4v4L3Ah4Gnga8A57r7DRm7fQe4Avgp8C+gHninu8d6JI9G74qIlAbTnUgGuoNbW1tbaWxsLMhn\ntnb0cPA37qGnz7n73CPZe1LBrsAREZFBtLW10dTUBNAUjp8Zkli3SEtZU02Ko/aaAMDtT6pVKiJS\nrBSkEdo2enel7lEqIlKkFKQRevu+E6hJJXl1w1aeWtEadTkiIjICCtII1VZWcMy+6t4VESlmCtKI\nnRhOGXjHwlX096t7V0Sk2ChII3bUXuNpqKpgVWsnjy7d9NpvEBGRWFGQRqw6leTY1wXz66t7V0Sk\n+ChIY+DEA4Ibfv/pqVX09vVHXI2IiAyHgjQGjthzHGNqU6zf0s0jizZGXY6IiAyDgjQGUskEx+8f\ntErVvSsiUlwUpDGRnnv3zqdX0d2r7l0RkWKhII2JN80ay4SGKto6e/n7S+uiLkdERIZIQRoTyYTx\n7+reFREpOgrSGElPznDPs2vo6O6LuBoRERkKBWmMvHFGM1Oba2jv7uPPz66OuhwRERkCBWmMmBkf\nOHg6AFf97WVNGSgiUgQUpDFz2hEzaaiu4MU1W7jrGbVKRUTiTkEaM001KT52xCwAfnDvS2qViojE\nnII0hj72llk0VFfwwprNapWKiMScgjSGmmpSnB62Si//i1qlIiJxpiCNqTOOmEVDVQXPr97M3WqV\niojEloI0pppqU5x+xEwAfqBWqYhIbClIY+xjb9nWKtV1pSIi8aQgjbHm2sqBVukl97xIj+5VKiIS\nOwrSmDvjLbMZU5vixTVb+OXDr0ZdjoiIZCmqIDWzC8zMzeyyjHVmZheZ2Soz6zCze81sTpR15lNT\nbYr/OW4fAC6950XWbu6MuCIREclUNEFqZocAnwQWZm36AnAOcBZwKNAO3G1m1YWtcPR88JDpvH5a\nE5u7evnWnc9HXY6IiGQoiiA1s3rgBuBMYFPGegPOBb7h7re5+0Lgo8AU4KQoah0NyYRx0XvmYga/\nf2wFC5ZsjLokEREJFUWQAlcBf3T3e7PWzwImAQPr3b0VmA8cvrODmVmVmTWmF6BhFGrOqwOnN/PB\ncEL7r9z2DH26HEZEJBZiH6Rm9iHgjcC8QTZPCh/XZK1fk7FtMPOA1oxleY5lFsT/HLc3jdUVPLeq\njRvma+CRiEgcxDpIzWw68APgFHfP5yibi4GmjGVaHo89anarr+J/jtsbgO/d/YIGHomIxECsgxQ4\nCJgAPGZmvWbWCxwFnBM+T7dEJ2a9byKw0xkM3L3L3dvSC7B5FGofFScfujuvm9JIW2cvZ/3yUbp6\n+6IuSUSkrMU9SP8C7A8cmLEsIBh4dCCwiCAwj0m/ITzneSjwcKGLLYRkwrjy5DfSWF3BY0tb+NIt\nT+Ou86UiIlGJdZC6+2Z3fzpzIbi8ZUP42oHLgC+b2bvNbH/gF8BK4NYISx9Vs8bVcdUpbyRh8LtH\nl/PzBxf17GFuAAAXo0lEQVRHXZKISNmKdZAO0XeAK4CfAv8C6oF35vmcauy8dc54vnzCfgB880/P\n8bcX1kZckYhIeTJ1Cw50B7e2trbS2NgYdTlD5u5ccPNT3LhgGQ1VFdxy9hHsOaE+6rJERIpSW1sb\nTU1NAE3h+JkhKYUWadkyM75+0lwOmTmGzV29nHrNP1m+aWvUZYmIlBUFaZGrrEjwo48cxKxxdaxo\n6eDkq+ezqrUj6rJERMqGgrQEjKuv4ldnHsqMsbUs3biVk6+ez9q2kj5FLCISGwrSEjG5qYZfnXko\nU5trWLy+nQ9f/QjrNndFXZaISMlTkJaQaWNq+fWZhzG5qZpX1rXzkZ/NV5iKiIwyBWmJmbFbLb86\n8zAmNFTxwprNvP/H/2DZRg1AEhEZLQrSEjRrXB03ffJwpo2pYcmGrfznj//Bi2uKZhZEEZGioiAt\nUTPH1fG7s97MXhPrWdPWxQd+8jCPL9302m8UEZFhUZCWsElN1dz0ycM5cHozLVt7OOVn8/nr89l3\nnBMRkVwoSEtcc20lN3z8UN46Zxxbu/v42LULuPhPz9HT1x91aSIiJUFBWgbqqir42akH89HDdwfg\nJw8s4gM/eViDkERE8kBBWiaqKpJc9J65/Pgjb6ShuoLHl7ZwwuV/566nV0VdmohIUVOQlpl3zp3M\nn855KwdObw5uDn79Y5z/u4Vs6eqNujQRkaKkIC1D08fW8tuzDueso/bADG5csIx//8HfefRVjeoV\nERkuBWmZSiUTXHD8Pvz6zMOY2lzD0o1bef+P/8H3//yCBiKJiAyDgrTMHTZ7N+4896289w1T6Xe4\n4q8vc+IVD+qaUxGRIdKNvSneG3vn2x0LV/KVW59m09YezOCjh+3Ofx+3Nw3VqahLExEZdSO9sbeC\nFAVppo3t3Xzjj8/y+8dWADCpsZqvnbgf75w7CTOLuDoRkdGjIM2BgnRHD760ni/e8hRLw2tN95vc\nyDnHzOHY/SaSSChQRaT0KEhzoCAdXEd3Hz+872WueXAx7d19AOwzqYHP/tscjp87SYEqIiVFQZoD\nBemubWrv5pqHFnPtQ0vYHF5vOnt8HWcduQcnvWEqlRUasyYixU9BmgMF6dC0bu3h//6xmGseXExb\nZxCokxqr+fhbZ/GhN82gvqoi4gpFREZOQZoDBenwbOnq5dfzl/KzBxexpq0LgMbqCj5y2O6c9uaZ\nTGisjrhCEZHhU5DmQEE6Ml29fdz6+Ap+cv8iFq1vB6AymeCkN0zhzLfOZs7EhogrFBEZOgVpDhSk\nuenvd+55bg1XP7CIBRnTDL5xRjPvOXAqJ7x+MuPqqyKsUETktZVkkJrZPOB9wD5AB/AP4Hx3fyFj\nHwP+FzgTaAYeAj7l7i8N43MUpHny6KubuPqBRfz52dX0h79ayYTxlj3H8Z4Dp3Ds6ybpXKqIxFKp\nBuldwG+AfwEVwDeBucB+7t4e7nM+MA84FVgMfB3YP9ync4ifoyDNs7Vtndy+cBV/eGIFTy5vHVhf\nnUrwjv0m8Z4DpnDkXuM14ldEYqMkgzSbmY0H1gJHufsDYWt0JfB9d/9euE8TsAY4zd1/M8TjKkhH\n0aJ1W7jtiZX84cmVLA7PpUIwQOmw2bsNLPtMatC1qSISmXIJ0j2Bl4D93f1pM5sNvAK8wd2fyNjv\nfuAJd/+vnRynCsg8adcALFeQji5356kVrdz2xEpuf3Ilazd3bbe9uTbFm/fYjaP2Gs+Re41nclNN\nRJWKSDkq+SA1swTwB6DZ3d8SrnszwTnRKe6+KmPfmwB39w/u5FgXAl/LXq8gLZy+fufJ5S3MX7SR\nRxZtYMGSjQOzJ6XtNbGet84ZzyEzx3DQ7mMZ36ABSyIyesohSH8EHA+8xd2Xh+tGGqRqkcZMT18/\nT61o5e8vruf+F9fyxLKWgcFKabvvVstBu4/hTTPHcsisscweV6eJ9EUkb0o6SM3sSuA9wJHuvjhj\n/Yi6dgc5vs6RxkzL1m7+/tL6sLW6iRfXbib7V3VcfSWHzBzLwTPHcsC0Jvab0khtpUYEi8jIlGSQ\nhoOJrgDeCxydfUlLxmCj77n798N1jQQDkjTYqIS0dvTw2NJNLFiykX8t3sQTy1vo7u3fbp+EwZwJ\nDew/rYm5Uxp53dQm9p3cqMttRGRISjVIfwicTNAafSFjU6u7d4T7nA9cwPaXv7weXf5S0jp7+nhq\nRSv/XLyRx5e28NSKloHpCjOZwczd6pgzoZ7JTdVMaqoJH6vZd1IjTbW6abmIBEo1SHdW3Onufm24\nT3pChk8QTMjwIPBpd39xGJ+jIC0Ba9o6eWp5KwtXtPLsylaeWdnGqtZd/y01e1wdB0xv5oBpTew9\nqZEpzUHIVlUkC1S1iMRFSQZpoShIS9eGLV08u6qNJevbWdXayeq2Tla3drJ041aWb+rY6fvG1Vcx\ntbmaPcbXs+fEevaa0MCcifVMG1NLUte6ipQkBWkOFKTlaWN7NwuXt/DkslaeWLaJJRu2srKlg66s\nc6+ZUkljanMN08fWMmNsLdPH1gZdxY3VTG6qYWJTlVqzIkVKQZoDBamkuTubtvawsqWD5Zu28sq6\ndl5cs5mX1mzhlXVbdhmyabvVVTKxsZrJTdVMbKpmcmM1U8fUMLW5hqljapjUWE1FUlMjisSNgjQH\nClIZir5+Z01bJ8s2bmXpxq0s27iVZZs6WNXawerWTla1dg4paJMJY1JjNdPH1jBtTC3TxtQwpamG\nMXWVjKlN0Vxbydi6SppqUupGFikgBWkOFKSSD+5Oy9aegfOw6cdVrR2saOlg+aYOVrZ00NM3tP/n\nzKC5JsWYukp2q6tkTG0lu9UHITu2rord6iqpr6qgtipJXWUFdVUVjA3DWBNViAyfgjQHClIplP5+\nZ+3mLla0BIOdloWDnla1dtKytZtNW3vYtLWbzZ29I/6M+qoKpo+tZfextUwfW8OEhmp2q69kt/oq\nxtVX0lidoqYySU0qWHSjAJGAgjQHClKJm56+flq29rCxvTtj6WJD+HxDezeb2rtp7+5ja1cvW7v7\n2NLVS2tHz7A/q6oiQWNNisbqivAxRWNNiuaaFE3h0lhTQU1lxUD41lQmaaqpYGxdlbqgpWSMNEg1\n5YtIDKWSCcY3VA17ov7Onj6Wb+pg6cZ2lm4IzuGu39LFhi3drN/Sxfot3Wzu7NnuXG5Xbz/rNnex\nbvOOE1oMRcKgubaS5toUDdUpGqoqqK+qoL66grrKJNWVSWpTFdQOPE8OtIirU0nqqpLUVlZQV5Wk\nrqqCusoKBbMUFQWpSAmpTiXZc0I9e06o3+V+/f1OV28/HT19tHf1srkzaM22dfYEjx3BY3rZ3NlL\nR3cfHT19dPb0sbW7j5at3bR19tLvDLSa86WuMkljTYqG6goaqlNUpxJUJhNUVSSprEhQkTDIyNqE\nGY3VqXCwVoqm2koaqiqoqkhQlQreV1WRIJVMkKpIkEradsdTcEsuFKQiZSiRsKBVWJlkbF3liI/T\n09fPpq1BiG5q7wlCuauHLZ29tHX2DoRuR08fnd3bnqdDeWt30C3d3tVLe3cffeEtf9q7+2jv7mNV\na76+8a6lkkZVRZJU0nCCPzQccA/ukzu5KbhOeHJTcL45YYaZkbAgxJMJI5U0UskEFckESTOyx3sZ\nwc89Eb7PDIxtfxAYwYjuYLuFz7OOYYSfu+2zE2YkEpA0Gzi+ZewP4X4JC/cJXvf3O33u9PU77kFt\nFeklGfxxkf3nRbrm9HEHvkPGZ2377O3fP9yTiCP90yaKc/4KUhEZsVQywYSGaiY0VOd8LHenu6+f\nLZ1BC3lzZy9tnUFruKu3j67efrp7++nq7aevf/vLjHr7nbaOXlq2dtMSDtja2t038L7OnuCxty/4\njOwbHvT0OT19gw/w2tLVG86CtSnn7yija86Eeu4576iCf66CVERiwSxoFVbVJ9mtfnRv4u7u9Pb7\nQDB39fbR2dNPT19/2FoMWnYQdFuvau1gVUvnwOjqfnf6nYHWa29/ENI9/U5Pbz99gwzi9PA9A+/1\noCXohI8D28L9wlZxpv7wPUErMmhRpvfNbF0O9rl97mGtwQ4JC1rAZkFLtc+d3r7+He4DLK9NQZov\n7uzQlyMisWS2rSu27jUye9a4OmBMQeoqFHff6bXG/f1OT38//YPMLTIQ+uEx0pk7EN6+/X7pVYN1\nEe+6vuzPHZpkRP8GK0jz5dnb4O/fh31OCJaJcxWsIhJLu5qwI5EwqhKaL3o4FKT58vwfYfXCYLnv\nYmieAXufALOPhgn7QtN0SGh+VRGRUqMJGcjThAzt6+HFu+D5P8Erf4HerPtgVtbD+L1h/D7QNA0a\np4bLFKgbDzXNkNRNpkVEoqKZjXKQ95mNutvhlb/BC3+ClY/D+pegfwgzzqTqgkCtbobKOqisDQI4\nVRu8rqqHygaoaoBUTdh1nDEOvb8X+nqDz+rrgUQSaneD2nFQtxvUjIVkJVhi23u3e247HnPbT2n7\nde6A73gyI/NYIx7AnqWiGpLqPBGR0aUgzcGoTxHY1wMbF8Ha54JQbVsBbSuDx9bl0NmS/88sJafc\nDHPeHnUVIlLiNEVgnCVTYbfu3oNv7+uFrjbo2BSEamdr0Krt3go97cHzri3QvQW6NgePPR3bD5Vz\nh0RF0HJLpIKWZ38PbN0QLO3hY39vuH//tlaliIiMmII0DpIVUDs2WKKQvogtHbDpdQNj2bPDNhzQ\nvl03LlndvXkM6OTIZ94RERltClLJODcKoGHvIiLDoesxREREcqAgFRERyYGCVEREJAcKUhERkRwo\nSEVERHJQMkFqZmeb2RIz6zSz+Wb2pqhrEhGR0lcSQWpmHwQuAf4XeCPwJHC3mU2ItDARESl5JRGk\nwHnA1e7+f+7+LHAWsBX4WLRliYhIqSv6IDWzSuAg4N70OnfvD18fvpP3VJlZY3oBGgpSrIiIlJyi\nD1JgHMF0PGuy1q8BJu3kPfOA1oxl+ahVJyIiJa1cpwi8mOCcaloDsLytbciT/YuISIkZaQaUQpCu\nB/qAiVnrJwKrB3uDu3cBXenXZtYAMH369FEqUUREikgDUD63UXP3bjN7FDgGuBXAzBLh6yuHeJiV\nwDRg8zA+uoGgS3i474tSsdVcbPVC8dVcbPWCai6EYqsX8ldzA0EmDFnRB2noEuA6M1sA/BM4F6gD\n/m8ob/bg7uYrhvOBNnC3FDYP5wawUSq2moutXii+moutXlDNhVBs9UJeax72e0siSN39RjMbD1xE\nMMDoCeCd7p49AElERCSvSiJIAdz9SobelSsiIpIXpXD5S1S6CGZS6nqtHWOk2Goutnqh+GoutnpB\nNRdCsdULEdZswelBERERGQm1SEVERHKgIBUREcmBglRERCQHClIREZEcKEhHIM43ETezI83sdjNb\naWZuZidlbTczu8jMVplZh5nda2ZzIqx3npn9y8w2m9laM7vVzPaOec2fMrOFZtYWLg+b2fFxrTeb\nmV0Q/m5clrEuVjWb2YVhjZnL83GtN6OuqWZ2vZltCOt6yswOztgem7rDf8Oyf8ZuZlfFrdaMmpNm\n9nUzWxzW9IqZfcUyZmOIom4F6TBZ/G8iXkdQ09k72f4F4ByCe7YeCrQT1F9dmPJ2cBRwFXAY8A4g\nBfzZzOoy9olbzcuBCwhu33cw8FfgNjN7Xbg9bvUOMLNDgE8CC7M2xbHmZ4DJGctbMrbFrl4zGwM8\nBPQAxwP7AZ8HNmXsFqe6D2H7n+87wvW/DR/jVGva+cCngM8A+4avvwB8NmOfwtft7lqGsQDzgSsz\nXicIphe8IOraBqnVgZMyXhuwCvjvjHVNQCfwoajrDesZH9Z9ZLHUHNa0ETgjzvUC9cCLwNuB+4DL\n4vozBi4EntjJttjVG9bwLeDvu9gey7ozarkMeDmsM5a1AncAP89adzNwfZQ/Y7VIh8FGcBPxmJlF\nMIViZv2tBH8cxKX+pvBxY/gY65rDrqYPEfQEPEy8670K+KO735u1Pq41z7HgFMUiM7vBzGaE6+Na\n77uBBWb22/A0xeNmdmbG9rjWnf637SPANR6kT1xr/QdwjJntBWBmBxD0VNwZbo+k7pKZIrBAdnUT\n8X0KX86wpW90PpyboBeMBXftuQx4yN2fDlfHsmYz258gOKuBLcB73f1ZM3tzRn2Zoq73QwSnIg4Z\nZHMcf8bzgdOAFwi6Hb8G/N3M5hLPegFmE3Q7XgJ8k+BnfbmZdbv7dcS3boCTgGbg2vB1XGv9FtAI\nPG9mfQT/Hn/J3W8It0dSt4JU4uQqYC7bnwuLqxeAAwla0P9JcPeho6ItaXBmNh34AfAOd++Mup6h\ncPc7M14uNLP5wKvAB4DnoqnqNSWABe7+xfD142HwnwVcF11ZQ3IGcKe7D+v2YRH4AHAKcDLBOfQD\ngcvMbGX4x0ok1LU7PMO+iXjMpGuMXf1mdiXwLuBt7r48Y1Msa3b3bnd/2d0fdfd5BAO8/ot41nsQ\nMAF4zMx6zayXYJDXOeHz9F/vcap5O+7eQnB+d0/i+TOG4Nzcs1nrngPSXdKxrNvMdic4b/6zjNWx\nrBX4LvBtd/+Nuz/l7r8ELgXmhdsjqVtBOgzu3g2kbyIObHcT8YejqmsYFhP8MmXW30gwsi2S+sOh\n6lcC7wX+zd0XZ+0Su5p3IgFUEc96/wLsT/DXe3pZANwQPl9E/GrejpnVE4ToKuL5M4ZgxO7eWev2\nImhJQ3zrPh1YC/wxY11ca60FerPW9bEty6KpO+qRYsW2AB8kGAF2KsHw658QDG+fGHVtYX31bPvH\n0oHPhc9nhNvPD+t9N8E/rrcS/ENaHVG9PwRaCFpIkzKWmox94lbzxcCRwMywnouBfoKu09jVu5Pv\ncB/hqN041gx8L/ydmAm8GbgHWAeMj2O9YU2HEFz68kWC0D+Z4NKLU2L8c04QBP23BtkWq1rDmq4l\nuPzshPB3473h78W3o6w7kh9GsS8E1zC9SnC7nvnAoVHXlFHb0QQBmr1cG243ghugryb4g+BeYK8I\n6x2sVgdOy9gnbjX/HFgS/vdfG9bzjrjWu5PvkB2ksaoZ+A2wMvwZLw9f7xHXejPqehfwVFjTc8CZ\nWdtjVTdwbPj/2w41xK3WsKYGggGJrwIdwCvAN4DKKOvWbdRERERyoHOkIiIiOVCQioiI5EBBKiIi\nkgMFqYiISA4UpCIiIjlQkIqIiORAQSoiIpIDBamIiEgOFKQiMmJmdrSZuZk1R12LSFQUpCIiIjlQ\nkIqIiORAQSpSxMwsYWbzzGyxmXWY2ZNm9p/htnS36wlmttDMOs3skfBm05nH+A8ze8bMusxsiZl9\nPmt7lZl928yWhfu8bGZnZJVykJktMLOtZvYPM8u+nZhIyVKQihS3ecBHgbOA1xHc5Ph6MzsqY5/v\nAp8nuM3XOuB2M0sBmNlBwE0Ed1fZH7gQ+LqZnZbx/l8AHwbOIbh14MeBLVl1/L/wMw4muF/kNfn6\ngiJxp7u/iBQpM6sCNgJvd/eHM9b/jOAGyD8F/gZ8yN1vDLeNJbgt2WnufpOZ3UBwj89jM97/HeAE\nd3+dme0FvEBwm7h7B6nh6PAz3u7ufwnX/TvBTaJr3L1zFL66SKyoRSpSvPYkCMx7zGxLeiFooe6R\nsd9AyLr7RoJg3DdctS/wUNZxHwLmmFmS4KbwfcD9r1HLwoznq8LHCcP4LiJFqyLqAkRkxOrDxxOA\nFVnbutg+TEeqY4j79WQ8T3dz6Q91KQv6RRcpXs8SBOYMd385a1mWsd9h6SdmNgbYC3guXPUccETW\ncY8AXnT3PuApgn8njkJEBqUWqUiRcvfNZvY94FIzSwAPAk0EQdgGvBru+lUz2wCsIRgUtB64Ndz2\nfeBfZvYV4EbgcOAzwKfDz1hiZtcB15jZOcCTwO7ABHe/qQBfUyT2FKQixe0rBCNx5wGzgRbgMeCb\nbOtxugD4ATAHeAI40d27Adz9MTP7AHBReKxVwFfd/dqMz/hUeLwfArsBS8PXIoJG7YqUrIwRtWPc\nvSXickRKls6RioiI5EBBKiIikgN17YqIiORALVIREZEcKEhFRERyoCAVERHJgYJUREQkBwpSERGR\nHChIRUREcqAgFRERyYGCVEREJAf/P03h70brrhALAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92ab864610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv_path_list = [\n",
    "    '../models/primary/emsize1000nhid1000dropout0.50tiedTruelr1.0/train.csv',\n",
    "    '../models/primary/finetune_middle_emsize1000nhid1000dropout0.50tiedTruelr1/train.csv',\n",
    "\n",
    "]\n",
    "label_list = [\n",
    "    '1',\n",
    "    '2'\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(5, 4), dpi=100)\n",
    "data = []\n",
    "for i, csv_path in enumerate(csv_path_list):\n",
    "    df = pd.read_csv(csv_path)\n",
    "    plt.plot(df.epoch.values, df.val_ppl.values, label=label_list[i])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('val ppl.')\n",
    "plt.title('primary')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [jupyter]",
   "language": "python",
   "name": "Python [jupyter]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
